Cybersecurity attacks have become a pervasive problem for organizations as many networked devices and other resources have been subjected to attack and compromised. A cyber-attack constitutes a threat to security arising out of stored or in-transit data that may involve the infiltration of any type of software for example, onto a network device with the intent to perpetrate malicious or criminal activity or even a nation-state attack (i.e., “malware”).
Recently, malware detection has undertaken many approaches involving network-based, malware protection services. One approach involves “on-site” placement of dedicated malware detection appliances at various ingress points throughout a network or subnetwork. Each of the malware detection appliances is configured to extract information propagating over the network at an ingress point, analyze the information to determine a level of suspiciousness, and conduct an analysis of the suspicious information internally within the appliance itself. While successful in detecting advanced malware that is attempting to infect network devices connected to the network (or subnetwork), as network traffic increases, an appliance-based approach may exhibit a decrease in performance due to resource constraints.
In particular, a malware detection appliance has a prescribed (and finite) amount of resources (for example, processing power) that, as resource capacity is exceeded, requires either the malware detection appliance to resort to more selective traffic inspection or additional malware detection appliances to be installed. The installation of additional malware detection appliances requires a large outlay of capital and network downtime, as information technology (IT) personnel are needed for installation of these appliances. Also, dedicated, malware detection appliances provide limited scalability and flexibility in deployment.
An improved approach that provides scalability, reliability, and efficient and efficacious malware detection at lower capital outlay is desirable.